Numerical Optimization and Operations Research in Python
Use data efficiently to support decision-making exploring Operations Research and Optimization in Python
Ratings 4.86 / 5.00
What You Will Learn!
- Gain proficiency in solving optimization problems using popular solvers, and learn to interpret and implement their results effectively
- Learn and apply useful modeling techniques to classical operations research problems
- Identify and formulate real-world problems as numerical optimization models
- Complete a case study on how to combine operations research and software engineering to build powerful solutions
Description
Numerical Optimization and Operations Research in Python
Use data efficiently to support decision-making by applying numerical optimization and operations research concepts seen throughout this comprehensive course. It combines theoretical foundations and practical coding applications, designed to empower you with the skills needed to tackle complex problems in a professional or academic context.
You will learn:
Theory:
Principles of Mathematical Optimization
Linear programming (LP)
Integer and Mixed-integer linear programming (MILP)
Handle infeasible scenarios
Multi-objective hierarchical (lexicographic) formulations
Constructive Heuristics and Local Search
Software:
Pyomo
Google OR-Tools
HiGHS
Problems:
Knapsack
Product-Mix
Transportation
Lot-Sizing
Job-Shop Scheduling
Facility Dispersion
Traveling Salesman
Capacitated Vehicle Routing Problem
Industry-Grade Skills: By the end of this course, you'll be able to formulate and solve your own optimization problems, a highly sought-after competency in industries ranging from logistics to finance. You'll also be able to convert your models into scalable programs for your company or team even though they are not familiar with optimization.
Who is this course for?
Data scientists and engineers who want to add optimization skills to their toolkit.
Professionals in logistics, supply chain management, or finance, who are looking to leverage optimization for decision-making.
Academics and students seeking a practical application of operations research and optimization theories.
Course Features:
More than 4 hours of comprehensive video lectures explaining concepts in a clear and engaging manner.
13+ Interactive Python notebooks for hands-on practice (and corresponding solutions).
Carefully selected articles and external references to improve your learning experience.
Access to a community forum for discussion and networking with fellow learners.
Lifetime access to course materials, including future updates.
Embark on this journey to master decision-making using optimization in Python. Whether you aim to advance your career, academically explore operations research, or simply enjoy the thrill of solving complex problems, this course is your gateway to new possibilities.
Who Should Attend!
- Professionals in pursuit of essential quantitative decision-making skills
- Academics eager to learn practical software skills to apply optimization theory